The present application relates to analyses of physiological signals for disease diagnosis and health enhancement, and in particular, to quantitative analysis of respiratory sinus arrhythmia.
The autonomic nervous system, comprised of sympathetic and parasympathetic components, plays a major role in maintaining a system's homeostasis with flexibility and stability. Deterioration of the autonomic-related regulatory mechanisms, especially the parasympathetic-mediated ones, has been recognized in various diseases, including fatal diseases such as acute myocardiac infarction, and chronic systemic diseases such as congestive heart failure, hypertension, and diabetes, as well as in physiologic aging process. To quantitatively evaluate the autonomic function, external perturbations are frequently applied to elicit the corresponding responses of specific physiological mechanisms. For example, cardiovascular parameters (e.g., heart rate and blood pressure) alter in response to valsalva maneuver. Alternatively, autonomic function can also be assessed without external perturbation. One example is by evaluating respiratory sinus arrhythmia (RSA), that is, the instantaneous variations in heart rate induced by breathing (which are mainly attributed to respiratory elicited wax and wane of changes in vagal activities). The greater the cyclic variations of the heart rate fluctuation, the stronger the vagus nerve activities. The noninvasive nature of assessing RSA provides an attractive option to quantify the parasympathetic function without potential complication.
RSA is a naturally occurring variation in heart rate that occurs during a breathing cycle. RSA is also a measure of parasympathetic nervous system activity—which denotes “rest and digest” behaviors. Vagal tone cannot be directly measured. Instead, other biological processes are measured that represent the functionality of vagal tone. An increase in vagal tone both slows the heart and makes heart rate more variable (i.e. there is more beat-to-beat change between heart beats). During the process of RSA inhalation temporarily suppresses vagal activity, causing an immediate increase in heart rate. Exhalation then decreases heart rate and causes vagal activity to resume. RSA is pronounced in children, but it typically decreases as a person approaches their teenage years. However, adults in excellent cardiovascular health, such as endurance runners, swimmers, and cyclists, are likely to have a more pronounced RSA.
Respiratory-related oscillations of heart rate dynamics can be quantified by various methods, among which, power spectrum analysis is the most widely used approach. Power spectrum analysis is based on the simple mathematical assumption that a temporal fluctuation can be modeled by a set of superimposed sinusoidal oscillations. The temporal change of heart rate in response to spontaneous physiological, mechanical, or pharmacological perturbations can be estimated in the frequency domain. Therefore, the amount of spectral power within a specific frequency band is considered to represent the corresponding changes of the underlying mechanisms or their responses to the interventions. The spectral power of heart rate time series in a high frequency (HF) band is used to quantify the RSA. The high frequency band corresponds to the total variance of inter-beat R-R interval oscillations within normal breathing frequency ranging from 0.15 Hz to 0.40 Hz (2.5-6.7 seconds per cycle). The power spectrum analysis has been extensively employed in pharmacological, public health, and clinical fields as functional index of vagal activities.
However, power spectral analysis is unreliable in assessing RSA properties because the assumption of the method is not valid in real-world signals. First, the frequency range of the HF band constrains breathing rate within 9-24 breaths/min. In reality, the periods between breaths vary over time. The breathing activities are not sinusoidal oscillations, which introduces spurious energy spreading around the dominant frequencies and projects spectral power to outside the HF band. Secondly, RSA is not merely a phenomenon in which heart rate increases and decreases in response to respiration; RSA is also influenced by complex interactions among central, neural, hormonal, and mechanical feedback mechanisms. The complex interactions can cause intermittent interdependence between cardiac and respiratory systems, which also cause the spurious energy spreading outside the HF band. Thirdly, abrupt changes in heart rate fluctuations unrelated to RSA such as arrhythmia or irrelevant interferences such as noise can introduce power to all frequency bands of the power spectrum, thus contaminating the power in the HF band. Thus power spectral analysis based on a HF band is unreliable in estimating RSA.
There is therefore an urgent need for more accurate quantification of RSA fluctuations.